Repeatable runout estimation in a noisy position error signal environment

ABSTRACT

A system and method for estimating repeatable runout in a servo control system, such as a disc drive servo loop, is provided. The system includes a Kalman filter that is configured to receive position error signals from the servo control system and to estimate the repeatable runout in the position error signals.

The present application claims priority from, and is aContinuation-In-Part of, U.S. patent application Ser. No. 10/277,768 forZhang et al. entitled REPEATABLE RUNOUT COMPENSATION IN A DISC DRIVE,filed Oct. 22, 2002, now U.S. Pat. No. 6,847,503, and assigned to theassignee of the present invention.

FIELD OF THE INVENTION

The present invention relates generally to servo control systems. Inparticular, the present invention relates to estimating repeatablerunout in a servo control system, such as a disc drive servo controlloop.

BACKGROUND OF THE INVENTION

Servo control systems that maintain the position of read/write headsrelative to tracks on discs in disc drives, for example, are well known.To provide proper position control, such servo systems generate positionerror signals (PES) indicative of the position of the heads from servoinformation that is written to the discs during the manufacturing of thedisc drive. In response to the detected position, the servo systemoutputs current to an actuator motor (such as a voice coil motor, orVCM) utilized to pivot an actuator assembly that moves the heads acrossthe disc surfaces.

It is a continuing trend in the disc drive industry to providesuccessive generations of disc drive products with ever increasing datastorage capacities and data transfer rates. Because the amount of discsurface area available for the recording of data remains substantiallyconstant (or even decreases as disc drive form factors become smaller),substantial advancements in areal recording densities, both in terms ofthe number of bits that can be recorded on each track as well as thenumber of tracks on each disc, are continually being made in order tofacilitate such increases in data capacity.

The servo information used to define the tracks is written during discdrive manufacturing using a highly precise servo track writer. While thetracks are intended to be concentric, uncontrolled factors such asbearing tolerances, spindle resonance modes, misalignment of the discsand the like tend to introduce errors in the location of the servoinformation. Each track is thus typically not perfectly concentric, butrather exhibits certain random, repeatable variations which aresometimes referred to as written-in repeatable runout, or WI-RRO. Inaddition to the WI-RRO, repeatable disturbance in a disc drive alsooccurs due to an unbalanced spindle, for example. The WI-RRO and otherrepeatable disturbances are collectively referred to as repeatablerunout (RRO). RRO appears as a component of the PES. Another componentof the PES called non-repeatable runout (NRRO) occurs due tonon-repeatable disturbances such as resonance modes, disc flutter,windage, disc vibrations, etc. RRO has a constant period determined bythe spindle speed of the disc drive, and NRRO is random, but withconsistent probability distributions on different tracks of discs of thedisc drive.

While RRO has previously had a minimal impact upon the operation of thedisc drive servo system, RRO has an increasingly adverse affect ashigher track densities are achieved. Particularly, RRO can ultimatelylead to an upper limit on achievable track densities, as RRO cuts intothe available track misalignment budget and reduces the range over whichthe servo system can provide stable servo control. Therefore, relativelyaccurate RRO measurements need to be carried out for PES analysis duringthe manufacture of disc drives with high track densities.

Techniques for measuring or estimating RRO usually determine RRO valuesfor sectors of each track by averaging the PES for sectors of each trackover several disc revolutions. Since NRRO is also present in the PES,and since the NRRO may be relatively large before the head properlysettles over a track, such an averaging technique may produce inaccurateresults.

Embodiments of the present invention provide solutions to these andother problems, and offer other advantages over the prior art.

SUMMARY OF THE INVENTION

Disclosed are apparatus and methods for estimating repeatable runout ina servo control system, such as a disc drive servo loop. The systemincludes a Kalman filter that is configured to receive position errorsignals from the servo control system and to estimate the repeatablerunout in the position error signals.

Other features and benefits that characterize embodiments of the presentinvention will be apparent upon reading the following detaileddescription and review of the associated drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of a disc drive.

FIG. 2 is a top view of a section of a disc showing an ideal track and arealized written-in track.

FIG. 3 is a simplified block diagram of a disc drive servo loop coupledto a RRO estimator of the present invention.

FIGS. 4-9 are plots illustrating results obtained by employing a RROestimation technique and the RRO estimation technique of the presentinvention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

In the embodiments described below, an apparatus and method are providedfor estimating repeatable runout in a servo control system, such as discdrive servo loop. The estimation of repeatable runout is carried out bya Kalman filter that receives position error signals from the servocontrol system and estimates the repeatable runout in the position errorsignals.

Referring now to FIG. 1, a perspective view of a disc drive 100 withwhich the present invention is useful is shown. The same referencenumerals are used in the various figures to represent the same orsimilar elements. Disc drive 100 includes a housing with a base 102 anda top cover (not shown). Disc drive 100 further includes a disc pack106, which is mounted on a spindle motor (not shown) by a disc clamp108. Disc pack 106 includes a plurality of individual discs which aremounted for co-rotation about central axis 109.

Each disc surface has an associated slider 110 which is mounted in discdrive 100 and carries a read/write head for communication with the discsurface. In the example shown in FIG. 1, sliders 110 are supported bysuspensions 112 which are in turn supported by track accessing arms 114of an actuator 116. The actuator shown in FIG. 1 is of the type known asa rotary moving coil actuator and includes a voice coil motor (VCM),shown generally at 118. Other types of actuators can be used, such aslinear actuators.

Voice coil motor 118 rotates actuator 116 with its attached sliders 110about a pivot shaft 120 to position sliders 110 over a desired datatrack along a path 122 between a disc inner diameter 124 and a discouter diameter 126. Voice coil motor 118 operates under the control of aclosed-loop servo controller within internal circuitry 128 based onposition information, which is stored on one or more of the discsurfaces within dedicated servo fields. The servo fields can beinterleaved with data sectors on each disc surface or can be located ona single disc surface that is dedicated to storing servo information. Asslider 110 passes over the servo fields, the read/write head generates areadback signal, which in turn is used to generate position errorsignals (PES) that identify the location of the head relative to thecenter line of the desired track. Based on the PES, actuator 116 movessuspension 112 to adjust the head's position so that it moves toward thedesired position. Once the transducing head is appropriately positioned,servo controller 128 then executes a desired read or write operation.

Referring now to FIG. 2, a top view of a section 200 of a disc with anideal, perfectly circular track 202 and an actual track 204 is shown.Section 200 includes a plurality of radially extending servo fields suchas servo fields 206 and 208. The servo fields include servo informationthat identifies the location of actual track 204 along disc section 200.

Any variation in the position of a head away from circular track 202 isconsidered a position error. The portions of track 204 that do notfollow circular track 202 create written-in repeatable runout (WI-RRO)position errors. Track 204 creates WI-RRO errors because each time ahead follows the servo fields that define track 204, it produces thesame position errors relative to ideal track 202. Further, as notedabove, in addition to the WI-RRO, repeatable disturbance also occurs dueto an unbalanced spindle, for example. The WI-RRO and other repeatabledisturbances are collectively referred to as repeatable runout (RRO).

As mentioned above, techniques for measuring RRO usually determine RROvalues for sectors of each track by averaging the PES for sectors ofeach track over several disc revolutions. A commonly used formula forcomputing RRO by averaging PES is as follows:

$\begin{matrix}{{{RRO}(n)} = {\frac{1}{n}\left\lbrack {{{PES}(n)} + {{PES}\left( {n - 1} \right)} + {{PES}\left( {n - 2} \right)} + \ldots + {{PES}(1)}} \right\rbrack}} & {{Equation}\mspace{14mu} 1}\end{matrix}$where RRO(n) is the resulting RRO for a particular track aftercollecting PES for sectors of the track over n disc revolutions; PES(n)is a vector of PES collected for sectors of the track during the n^(th)disc revolution; PES(n−1) is a vector of PES collected for sectors ofthe track during the (n−1)^(th) disc revolution, etc. Theoretically, theestimated RRO(n) (Equation 1) converges to the true RRO value when PESis collected for each track over an infinite number of disc revolutions(n approaches infinity). However, in practice, the PES data for eachtrack is usually collected over only about 5-10 disc revolutions in manyapplications. Experiments have shown that when the amount of PES datacollected is not sufficiently large, the averaging scheme for RROcomputation (Equation 1) may produce erroneous results. This isespecially true when the non-repeatable runout (NRRO) to RRO ratio ishigh in the servo system.

Under the present invention, instead of employing a PES averagingtechnique for RRO calculation (such as the technique described inconnection with Equation 1), RRO estimation is carried out using aKalman filter that is configured to receive PES form the servo controlsystem and to estimate the repeatable runout in the PES. For reasonsprovided further below, this Kalman filter technique minimizes orreduces the time required to carry out relatively accurate RROestimation.

Referring now to FIG. 3, a simplified block diagram of a servo loop 300of a disc drive 100 connected to a manufacturing system 325, whichincludes a RRO estimator 330 of the present invention, is shown. Servoloop 300 includes servo controller 302 and disc drive actuator mechanics304. Servo controller 302 is the servo controller circuitry withininternal circuit 128 of FIG. 1. Drive actuator mechanics 304 includesactuator assembly 116, voice coil motor 118, track accessing arm 114,suspension 112, and sliders 110, all of FIG. 1.

Servo controller 302 generates a control current 306 that drives thevoice coil motor of drive actuator 304. In response, the drive actuator304 produces head motion 308. In FIGS. 3-1, RRO 310 is shown separatelyeven though the RRO would otherwise appear implicitly in head motion308. The separation of RRO from head motion 308 provides a betterunderstanding of the present invention. RRO 310 includes WI-RRO 312 andrepeatable disturbances d_(W) at 314, which are located at harmonicfrequencies due to disc motion or motor vibrations. In addition, noisein the servo system has been separated and shown as a first noisecomponent dn1 at 315, which represents non-repeatable torquedisturbances, such as windage, and a second noise component dn2 at 317,which represents analog to digital converter noise, electronic noise,etc. The combination of these signals results in the head's servomeasurement signal, represented by reference numeral 316. Servomeasurement signal 316 is subtracted from a reference signal 318, whichis generated by internal circuitry 128 based on a desired location ofthe head. Subtracting head measurement 316 from reference signal 318produces PES 320, which is input to servo controller 302.

During manufacture, after servo writing is carried out, the disc driveis operated in a test environment with RRO estimator 330 coupled toservo loop 300. RRO estimator 330 carries out a relatively rapid andaccurate estimation of RRO 310 in PES 320. As can be seen in FIG. 3, RROestimator 330 includes a Kalman filter module 332 and an output 334.Kalman filer 332 carries out the RRO estimation and provides estimatedRRO values to output 334, which may be a display unit, for example. Insome embodiments, output 334 may include a memory in which estimated RROvalues are stored. The RRO output may be provided in micro inchesrelative to or from a track center line, as a percentage of track pitch,etc. A discussion of the suitability of Kalman filters for RROestimation is provided below. Also provided are descriptions of ageneral Kalman filter algorithm and a specific example Kalman filtertype algorithm that can be utilized in module 332.

Evaluation of disc drive servo loop performance depends in part on howaccurately the RRO in the PES is estimated. From the viewpoint of astochastic process, RRO can be considered as a deterministic signalcorrupted by a normally distributed random signal, which is the NRRO. Itis therefore possible to use a stochastic estimation technique todevelop a RRO estimator. A good estimator should fully utilize theknowledge of the system, for example, the statistical description ofprocess noises. The Kalman filter is one of the best estimators for astatistical state estimation problem. The Kalman Filter is a well-knownalgorithm developed by R. E. Kalman in 1960. It is a recursive techniqueof obtaining the solution to a least squares fit. Given only the meanand standard deviation of noises, the Kalman filter is the best linearestimator. The Kalman filter considers a stochastic process governed bythe linear stochastic difference Equations 2A-2B:x(n)=Ax(n−1)+Bu(n)+w(n−1)  Equation 2Az(n)=Cx(n)+v(n)  Equation 2Bwhere x(n) is the system state; z(n) is the output measurement; u(n) isthe input of the process; A, B, C represent the process dynamic model;the random variables w and v represent the process and measurementnoise, respectively. In addition, w and v are assumed to be independentand with normal probability distributionsp(w)˜N(0,Q)  Equation 3Ap(v)˜N(0,R)  Equation 3Bwith constants Q and R being the process noise covariance andmeasurement noise covariance, respectively. The Kalman estimationproblem is to design an observer to estimate the state x(n) using thenoise corrupted measurement data z(n). The Kalman filter is a recursiveoptimal state estimator that has the following form

${K(n)} = \frac{{{AP}\left( {n - 1} \right)}A^{T}}{{{{CAP}\left( {n - 1} \right)}A^{T}C^{T}} + R}${circumflex over (x)}(n)=A{circumflex over(x)}(n−1)+Bu(n)+K(n){z(n)−C[A{circumflex over(x)}(n−1)+Bu(n)]}  Equation 5P(n)=[I−K(n)C][AP(n−1)A ^(T) +Q]  Equation 6Where {circumflex over (x)}(n) is the estimate of x(n); K(n) is theestimator gain; P(n) is called the state estimation error covariance.

For the RRO estimation problem, RRO can be considered as a state of alinear stochastic processRRO(n)=RRO(n−1)  Equation 7APES(n)=RRO(n)+NRRO(n)  Equation 7BComparing Equation 7 with Equation 2, it can be seen that by choosing:A=1, B=0, C=1  Equation 8Aw(n)=0, v(n)=NRRO(n)  Equation 8Bx(n)=RRO(n), z(n)=RRO(n)+NRRO(n)  Equation 8CEquation 7 can be viewed as a special class of stochastic processdescribed in Equation 2. Hence, from Equations 4-8 it follows that aKalman filter type of RRO estimation algorithm is:RRO estimator gain:

$\begin{matrix}{{K(n)} = \frac{P\left( {n - 1} \right)}{{P\left( {n - 1} \right)} + R}} & {{Equation}\mspace{14mu} 9}\end{matrix}$Estimation of the RRO:{circumflex over (x)}(n)=[1−K(n)]{circumflex over(x)}(n−1)+K(n)PES(n)  Equation 10RRO estimation error covariance:P(n)=[1−K(n)]P(n−1)  Equation 11where PES(n) is the n^(th) revolution of PES. It follows from Equation 9that when the NRRO covariance R is large, the estimator gain K(n)becomes small. This implies that when more NRRO noise is corrupted inthe PES, less confidence is had in the RRO information provided by PES.The estimator will place a small weight K(n) on the measured PES for theRRO estimate, and will set a large weight 1−K(n) on the previous{circumflex over (x)}(n−1) estimate.

Let {circumflex over (x)}(n) be the estimate of RRO at n-th revolutionof PES. It follows from Equation 1 that

$\begin{matrix}{{\hat{x}\left( {n - 1} \right)} = {\frac{1}{n - 1}\left\lbrack {{{PES}\left( {n - 1} \right)} + {{PES}\left( {n - 2} \right)} + \ldots + {{PES}(1)}} \right\rbrack}} & {{Equation}\mspace{14mu} 12} \\{{\hat{x}(n)} = {\frac{1}{n}\left\lbrack {{{PES}(n)} + {{PES}\left( {n - 1} \right)} + {{PES}\left( {n - 2} \right)} + \ldots + {{PES}(1)}} \right\rbrack}} & {{Equation}\mspace{14mu} 13}\end{matrix}$Equation 1 can be further expressed in a recursive form as:

$\begin{matrix}\begin{matrix}{{\hat{x}(n)} = {\frac{1}{n}\left\lbrack {{{PES}(n)} + {\left( {n - 1} \right){\hat{x}\left( {n - 1} \right)}}} \right\rbrack}} \\{= {{\left( {1 - \frac{1}{n}} \right){\hat{x}\left( {n - 1} \right)}} + {\frac{1}{n}{{PES}(n)}}}}\end{matrix} & {{Equation}\mspace{14mu} 14}\end{matrix}$By comparing Equation 14 and the new RRO estimation algorithm describedby Equation 10, it is seen that the RRO estimator described by Equation1 is a special form of the new RRO estimation algorithm described byEquation 10 with K(n)=1/n. Substituting Equation 9 into Equation 11leads to:

$\begin{matrix}{{P\left( {n - 1} \right)} = {{\left\lbrack {1 - \frac{P\left( {n - 2} \right)}{{P\left( {n - 2} \right)} + R}} \right\rbrack{P\left( {n - 2} \right)}} = \frac{{RP}\left( {n - 2} \right)}{{P\left( {n - 2} \right)} + R}}} & {{Equation}\mspace{14mu} 15}\end{matrix}$It follows from Equation 9 that

$\begin{matrix}{\frac{1}{K(n)} = {1 + \frac{R}{P\left( {n - 1} \right)}}} & {{Equation}\mspace{14mu} 16}\end{matrix}$Substituting Equation 15 into Equation 16 suggests that:

$\begin{matrix}{\frac{1}{K(n)} = {{1 + \frac{{P\left( {n - 2} \right)} + R}{P\left( {n - 2} \right)}} = {2 + \frac{R}{P\left( {n - 2} \right)}}}} & {{Equation}\mspace{14mu} 17}\end{matrix}$By repeating the above step, the following equation is obtained:

$\begin{matrix}{\frac{1}{K(n)} = {n + \frac{R}{P(0)}}} & {{Equation}\mspace{14mu} 18}\end{matrix}$Therefore, the RRO estimator described by Equation 1 and rewritten asEquation 14 is a special case of Equations 9-11 with the choice of theparameters satisfying R/P(0)→0. R/P(0)→0 implies that either R→0 orP(0)→∞. R→0 means that the noise variance is close to zero. This impliesthat the NRRO in PES is close to zero. Obviously, this assumption isincorrect because of the existance of NRRO in a disc drive. When theestimation error covariance is chosen such that P(0)→∞, it is shown fromEquation 9-11 that the learning factor of the first iteration K(1)→1,and therefore {circumflex over (x)}(1)→PES(1). This means that for thefirst RRO estimate, the estimated RRO equals to measured PES. In discdrives, typically about 40-60% of PES components are NRRO. Hencechoosing the initial condition P(0)→∞ is not adequate.

An advantage of the new RRO estimation algorithm is that the estimatorgain K(n) in Equation 10 is optimally chosen based on statisticalinformation developed in past manufacturing history of similar discdrives. The statistical information utilized includes statisticalinformation of NRRO. In disc drives, the NRRO distribution is measurableand consistent over different tracks, heads (even different drives).When such information is utilized, better RRO estimates are obtained.

The noise covariance R can be determined through the following steps:

-   (i) Select a track, and collect a number of revolutions of PES (for    example, 100 revolutions of PES).-   (ii) Calculate RRO using the averaging method of Equation 1.-   (iii) Calculate the standard derivation σ_(RRO) and σ_(NRRO) of RRO    and NRRO, respectively.-   (iv) Calculate the NRRO-to-RRO ratio (NRR), NRR=σ_(NRRO)/σ_(RRO).-   (v) Select other tracks at the disc inner diameter (ID), middle    diameter (MD) and outer diameter (OD) and repeat the above (i)-(iv)    to obtain NRR_(i) at different tracks.-   (vi) The noise covariance R of the drive can be calculated by

$\begin{matrix}{R\left\lbrack {\frac{1}{q}{\sum\limits_{i = 1}^{q}\;{NRR}_{i}}} \right\rbrack}^{2} & {{Equation}\mspace{14mu} 19}\end{matrix}$with q being the number of tested tracks.

The initial conditions {circumflex over (x)}(0) and P(0) can be selectedbased on the following guidelines:

-   (a) In disc drives, there is a large amount of coherent RRO existing    on adjacent tracks. {circumflex over (x)}(0) may be set as the RRO    estimate of adjacent tracks. If this information is not available,    {circumflex over (x)}(0) can be simply set as zero.-   (b) For a fast convergence of RRO estimation, P(0) should be chosen    based on the NRRO-to-RRO ratio and the initial state {circumflex    over (x)}(0). In general, if there is little confidence in the    accuracy of the initial state, a large P(0) should be chosen.

The above described Kalman filter algorithm (Equations 9-11) wasimplemented in RRO estimator 330 of the present invention and RROestimation tests were conducted on discs dives with different NRRvalues. To implement the Kalman filter algorithm, procedure (i)-(vi)presented above was used for determining noise covariance R, andguidelines (a)-(b) above were used for selecting {circumflex over(x)}(0) and P(0).

Case 1: RRO Estimation for a Disc Drive with NRR Close to 1.

In the test disc drive the measured standard derivations of NRRO and RROare 2.1% and 2.14% of track pitch, respectively. The true NRRO-to-RROratio is NRR=2.1/2.14=0.96. Hence, the best choice of noise covariance Rshould be 0.96. In order to test the robustness of the new Kalman filteralgorithm, it is assumed that the true NRR is not known. Only a roughlyestimated value is available. In this test, values of R=1.1 and P(0)=2were used.

FIG. 4 is a comparison of plots of RRO estimation results obtained usingthe RRO estimation technique (described by Equation 1) and the newKalman filter technique of the present invention (described by Equations9-11). The vertical axis represents RRO as a percentage of track pitchand the horizontal axis represents revolutions of PES. From FIG. 4, itis clear that the RRO estimate obtained using the new technique (plot402) is smaller than the RRO estimate obtained using the algorithm (plot404).

FIG. 5 includes plots showing the standard deviation of the true RRO(plot 506), the standard deviation of the RRO estimated using the newestimation technique (plot 502) and the standard deviation of theestimated RRO using the estimation technique (plot 504). The verticalaxis represents RRO as a percentage of track pitch and the horizontalaxis represents revolutions of PES. From FIG. 5, it is clear that thenew estimation technique provides a faster convergence of the standarddeviation of the RRO than the old technique.

FIG. 6 includes plots of the learning gain of the RRO estimationtechnique (plot 604) and the new RRO estimation technique (plot 602).The vertical axis represents the estimation gain and the horizontal axisrepresents revolutions of PES. FIG. 6 indicates that the new algorithmhas a small estimator gain at the beginning of the estimation process.As the number of PES revolutions increases, both estimator gains aresubstantially similar.

Case 2: RRO Estimation for a Disc Drive with NRR Close to 1.

In this case the test disc drive includes a servo loop that employs acompensation table for RRO errors to cause about a 50% RRO reduction.Therefore, the standard derivation of RRO is about 1.07% of track pitch.The standard deviation of NRRO is the same as in case 1. Consequently,the true NRR=1.96 in this case and therefore the ideal noise covarianceR should be 3.85. As in case 1, the true NRR is assumed to be unknownand a value of R=3 is used. Since it is known that the RRO is small andthe NRR is large, a relatively high initial noise covariance P(0)=3 isemployed.

The experiments carried out in Case 1 are repeated for the disc drive inCase 2 and similar plots are obtained. In FIGS. 7-9, plots 702, 802 and902 represent the new technique, plots 704, 804 and 904 represent theold technique and plot 806 is a plot showing the standard deviation ofthe true RRO. FIGS. 7 and 8 show that for a small number of PESrevolutions, the RRO estimation error obtained using the method is verylarge due to high NRRO components in the PES. Comparing FIGS. 6 and 9,it is seen that when NRR is large, the estimator gain of the newestimation algorithm decreases, while the method does not have amechanism to adjust the learning rate.

It is to be understood that even though numerous characteristics andadvantages of various embodiments of the invention have been set forthin the foregoing description, together with details of the structure andfunction of various embodiments of the invention, this disclosure isillustrative only, and changes may be made in detail, especially inmatters of structure and arrangement of parts within the principles ofthe present invention to the full extent indicated by the broad generalmeaning of the terms in which the appended claims are expressed. Forexample, the particular elements may vary depending on the particularapplication for the servo control system while maintaining substantiallythe same functionality without departing from the scope and spirit ofthe present invention. In addition, although the preferred embodimentdescribed herein is directed to an RRO estimation system for a discdrive servo loop, it will be appreciated by those skilled in the artthat the teachings of the present invention can be applied to otherservo control systems, without departing from the scope and spirit ofthe present invention. Further, the RRO estimator may be implemented inhardware or software. The disc drive can be based upon magnetic,optical, or other storage technologies and may or may not employ aflying slider.

1. An apparatus for estimating repeatable runout in a servo controlsystem, the apparatus comprising: a stochastic state estimator, whichutilizes deterministic and random signal characteristics of a positionerror signal and receives position error signals from the servo controlsystem and estimates the signal characteristics repeatable runout in theposition error signals.
 2. The apparatus of claim 1 wherein thestochastic state estimator includes a linear stochastic model ofrepeatable runout and non-repeatable runout.
 3. The apparatus of claim 1wherein the stochastic state estimator estimates the repeatable runoutbased on statistical information developed in past manufacturinghistory.
 4. The apparatus of claim 3 wherein the statistical informationdeveloped in past manufacturing history includes statistical informationof non-repeatable runout.
 5. The apparatus of claim 4 wherein thestatistical information of non-repeatable runout is utilized tocalculate noise covariance, which is utilized by the stochastic stateestimator to estimate the repeatable runout.
 6. The apparatus of claim 1further comprising an output device configured to output the estimatedrepeatable runout from the stochastic state estimator.
 7. The apparatusof claim 6 wherein the estimated repeatable runout output by the outputdevice is expressed as a percentage of track pitch.
 8. The apparatus ofclaim 6 wherein the estimated repeatable runout output by the outputdevice is expressed in micro inches relative to a track center line. 9.The apparatus of claim 6 wherein the stochastic state estimator is aKalman filter.
 10. The apparatus of claim 6 wherein the output deviceincludes a memory configured to store the estimated repeatable runoutoutput.
 11. A method of estimating repeatable runout in a servo controlsystem, the method comprising: providing a stochastic state estimator,which utilizes deterministic and random signal characteristics of aposition error signal and that receives position error signals from theservo control system and estimates the repeatable runout in the positionerror signals.
 12. The method of claim 11 wherein the stochastic stateestimator includes a linear stochastic model of repeatable runout andnon-repeatable runout.
 13. The method of claim 11 wherein the stochasticstate estimator estimates the repeatable runout based on statisticalinformation developed in past manufacturing history.
 14. The method ofclaim 13 wherein the statistical information developed in pastmanufacturing history includes statistical information of non-repeatablerunout.
 15. The method of claim 14 wherein the statistical informationof non-repeatable runout is utilized to calculate noise covariance,which is utilized by the stochastic state estimator to estimate therepeatable runout.
 16. The method of claim 11 further comprisingproviding an output device that outputs the estimated repeatable runoutfrom the stochastic state estimator.
 17. The method of claim 16 whereinthe estimated repeatable runout output by the output device is expressedas a percentage of track pitch.
 18. The method of claim 16 wherein theestimated repeatable runout output by the output device is expressed inmicro inches relative to a track center line.
 19. The method of claim 16wherein the stochastic state estimator is a Kalman filter.
 20. Anapparatus for estimating repeatable runout in a servo control system,the apparatus comprising: a stochastic state estimation means, whichutilizes deterministic and random signal characteristics, forcharacteristics of a position error signal for receiving position errorsignals from the servo control system and for estimating the repeatablerunout in the position error signals; and an output device configured tooutput the estimated repeatable runout from the stochastic stateestimation means.